TextRank (TR) is a graph-based ranking model [1]}. This model aims to identify relations between various entities in a text by incorporating the concept of text summarization [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. The task of text summarization is described in context of finding the relevant sentences in a text and extracting salient terms. The input to the model is a connected graph \(G = (V, E)\)  with the set of vertices \(V\)  and set of edges \(E\) , where \(E\)  is a subset of V\(\times \) V. The output of the model is a list of top-N terms with corresponding ranked scores extracted from a corpus. Vertices denote each candidate term. Edges between terms are added between two candidate terms based on their co-occurrence relation. The co-occurrence relation indicates the relationships between terms. In our context, we adapted the TextRank graph with undirected edges for each bug comment. An edge is added between two candidate terms in a comment. Then, the weight of the edge is computed where the two words co-occur relative to the number of comments. The TextRank score \(TR(V_i)\)  associated with a candidate term at vertex \(V_i\)  is iteratively updated as follows:
\(\small { TR(V_i)=1-d+d \sum \limits _{V_n \in E(V_i)} \frac{w_{ni}}{ \sum {V_m \in E(V_n) w_{nm}}} TR(V_n) }\) 
